code: def nanargmin(a, axis=None, out=None, combine_size=None):
      """
      Return the indices of the minimum values in the specified axis ignoring
      NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
      cannot be trusted if a slice contains only NaNs and Infs.
  
      Parameters
      ----------
      a : array_like
          Input data.
      axis : int, optional
          Axis along which to operate.  By default flattened input is used.
      combine_size: int, optional
          The number of chunks to combine.
  
      Returns
      -------
      index_array : Tensor
          A tensor of indices or a single index value.
  
      See Also
      --------
      argmin, nanargmax
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array([[mt.nan, 4], [2, 3]])
      >>> mt.argmin(a).execute()
      0
      >>> mt.nanargmin(a).execute()
      2
      >>> mt.nanargmin(a, axis=0).execute()
      array([1, 1])
      >>> mt.nanargmin(a, axis=1).execute()
      array([1, 0])
  
      """
      op = TensorNanArgmin(
          axis=axis, dtype=np.dtype(int), combine_size=combine_size
      )
      return op(a, out=out)
  
file_path: mars/mars/tensor/reduction/nanargmin.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField, TupleField
  from .core import TensorReduction, TensorArgReductionMixin
label_desc: Use image node_id 1 for calling a global function with example usage: nanargmin(a, axis, out, combine_size) and returns: op